Voice Authentication Using Multi-Dimensional Acoustic Feature Vectors
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Solution Overview
Problem
Conventional voice authentication systems are vulnerable to spoofing attacks, as they rely on voice attributes that can be mimicked by various algorithms, leading to potential unauthorized access.
Innovation Solution
A machine learning multi-dimensional acoustic feature vector authentication system that uses convolutional neural networks (CNNs) to extract and analyze acoustic features, converting them into multi-dimensional vectors for spoofing detection, employing multiple algorithms like Short-Time Fourier Transformation, Mel-Frequency Cepstral Coefficient Transformation, and Tonnetz space geometric transformation to differentiate between human and spoofed voices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional voice authentication systems use basic voice attribute comparison, then the system is simple to implement, but the system is highly susceptible to spoofing attacks
Solution Approach 1:
The patent transforms voice authentication from traditional time-domain signal comparison to frequency-domain spectral analysis. By converting voice signals to spectrograms and analyzing multi-dimensional acoustic feature vectors in the frequency domain, the system detects spoofing attacks that are invisible in the time domain, thereby improving reliability without excessive complexity increase
Solution Approach 2:
The patent replaces conventional mechanical voice comparison methods with machine learning-based acoustic feature analysis. Neural networks process multi-dimensional acoustic feature vectors to detect subtle patterns indicating spoofing, substituting simple algorithmic comparison with intelligent pattern recognition while maintaining system feasibility
2Measurement precision
If the system uses multiple acoustic feature extraction algorithms to detect spoofing, then the detection accuracy improves, but the processing time and computational complexity increase
Solution Approach 1:
The patent segments the voice authentication process into distinct stages: acoustic feature extraction, spectral transformation, and spoofing detection. Each stage uses specialized algorithms optimized for its specific task, allowing parallel processing and reducing overall processing time while maintaining high detection accuracy through focused analysis at each stage
Solution Approach 2:
The patent extracts multiple types of acoustic features (spectral, temporal, and cepstral characteristics) to provide comprehensive spoofing detection. By analyzing more features than the minimum required, the system achieves higher detection accuracy for various spoofing methods, accepting increased computational load as necessary for security-critical applications
Data Source
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AI summary
A machine learning multi-dimensional acoustic feature vector authentication system, according to an example of the present disclosure, builds and trains multiple multi-dimensional acoustic feature vector machine learning classifiers to determine a probability of spoofing of a voice. The system may extract an acoustic feature from a voice sample of a user. The system may convert the acoustic feature into multi-dimensional acoustic feature vectors and apply the multi-dimensional acoustic feature vectors to the multi-dimensional acoustic feature vector machine learning classifiers to detect spoofing and determine whether to authenticate a user.